# Introduction to Image Data with Python

# Using matplotlib and OpenCV!

import pandas as pd
import numpy as np

from glob import glob

import cv2
import matplotlib.pylab as plt

plt.style.use('ggplot')

Reading in Images

cat_files = glob('../input/cat-and-dog/training_set/training_set/cats/*.jpg')
dog_files = glob('../input/cat-and-dog/training_set/training_set/dogs/*.jpg')

img_mpl = plt.imread(cat_files[20])
img_cv2 = cv2.imread(cat_files[20])
img_mpl.shape, img_cv2.shape

((232, 350, 3), (232, 350, 3))

Image Array

(Height, Width, Channels)

pd.Series(img_mpl.flatten()).plot(kind='hist',
                                  bins=50,
                                  title='Distribution of Pixel Values')
plt.show()

Display Images

fig, ax = plt.subplots(figsize=(10, 10))
ax.imshow(img_mpl)
ax.axis('off')
plt.show()

Image Channels

RGB Channels

An RGB image has three channels: red, green, and blue. RGB channels roughly follow the color receptors in the human eye, and are used in computer displays and image scanners.

Source: Wikipedia#:~:text=RGB%20images,-An%20RGB%20image&text=If%20the%20RGB%20image%20is,intensities%20between%200%20and%20255.)

# Display RGB Channels of our image
fig, axs = plt.subplots(1, 3, figsize=(15, 5))
axs[0].imshow(img_mpl[:,:,0], cmap='Reds')
axs[1].imshow(img_mpl[:,:,1], cmap='Greens')
axs[2].imshow(img_mpl[:,:,2], cmap='Blues')
axs[0].axis('off')
axs[1].axis('off')
axs[2].axis('off')
axs[0].set_title('Red channel')
axs[1].set_title('Green channel')
axs[2].set_title('Blue channel')
plt.show()

Matplotlib vs cv2 Numpy Arrays

• cv2 reads in channels as BGR
• matplotlib reads in channels as RGB

fig, axs = plt.subplots(1, 2, figsize=(10, 5))
axs[0].imshow(img_cv2)
axs[1].imshow(img_mpl)
axs[0].axis('off')
axs[1].axis('off')
axs[0].set_title('CV2 Image')
axs[1].set_title('Matplotlib Image')
plt.show()

# Converting from BGR to RGB
img_cv2_rgb = cv2.cvtColor(img_cv2, cv2.COLOR_BGR2RGB)
fig, ax = plt.subplots()
ax.imshow(img_cv2_rgb)
ax.axis('off')
plt.show()

Image Manipulation

img = plt.imread(dog_files[4])
fig, ax = plt.subplots(figsize=(8, 8))
ax.imshow(img)
ax.axis('off')
plt.show()

img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
fig, ax = plt.subplots(figsize=(8, 8))
ax.imshow(img_gray, cmap='Greys')
ax.axis('off')
ax.set_title('Grey Image')
plt.show()

Resizing and Scaling

img_resized = cv2.resize(img, None, fx=0.25, fy=0.25)
fig, ax = plt.subplots(figsize=(8, 8))
ax.imshow(img_resized)
ax.axis('off')
plt.show()

# Different Size
img_resize = cv2.resize(img, (100, 200))
fig, ax = plt.subplots(figsize=(8, 8))
ax.imshow(img_resize)
ax.axis('off')
plt.show()

img_resize = cv2.resize(img, (5000, 5000), interpolation = cv2.INTER_CUBIC)
fig, ax = plt.subplots(figsize=(8, 8))
ax.imshow(img_resize)
ax.axis('off')
plt.show()

CV2 Kernels

# Sharpen Image
kernel_sharpening = np.array([[-1,-1,-1], 
                              [-1,9,-1], 
                              [-1,-1,-1]])

sharpened = cv2.filter2D(img, -1, kernel_sharpening)

fig, ax = plt.subplots(figsize=(8, 8))
ax.imshow(sharpened)
ax.axis('off')
ax.set_title('Sharpened Image')
plt.show()

# Blurring the image
kernel_3x3 = np.ones((3, 3), np.float32) / 9
blurred = cv2.filter2D(img, -1, kernel_3x3)
fig, ax = plt.subplots(figsize=(8, 8))
ax.imshow(blurred)
ax.axis('off')
ax.set_title('Blurred Image')
plt.show()

Save Image

plt.imsave('mpl_dog.png', blurred)
cv2.imwrite('cv2_dog.png', blurred)

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